Amanda Warlick
2019-09-29
## site lat long year week mo yday PG_count v temp mins
## 1 Cliffside 48.368 -122.6697 2017 32 8 220 15 -0.63 0.48 -1.25
## 2 Cliffside 48.368 -122.6697 2017 33 8 228 15 -1.24 0.90 1.06
## 3 Cliffside 48.368 -122.6697 2016 34 8 235 16 -1.12 0.93 -0.48
## 4 Cliffside 48.368 -122.6697 2014 27 7 188 14 -1.66 0.35 1.27
## 5 Cliffside 48.368 -122.6697 2010 27 7 184 23 -2.21 -0.73 -0.16
## 6 Cliffside 48.368 -122.6697 2008 31 8 217 23 -0.94 -1.23 -0.80
ggplot(data, aes(yday, temp)) +
geom_point(color = 'blue') +
xlab('Year Day') + ylab('Temperature') +
theme_classic() #other simple versions include theme_bw()ggplot(data, aes(yday, temp)) +
geom_point(aes(color = factor(mo))) +
xlab('Year Day') + ylab('Temperature') +
theme_classic() #other simple versions include theme_bw()ggplot(data, aes(yday, temp)) +
#try grouping color by year or site
geom_point(aes(color = factor(mo))) +
#try adding size = year inside aes()
# geom_point(aes(color = factor(mo), size = year, alpha = 0.2), show.legend = F) +
xlab('Year Day') + ylab('Temperature') +
theme(legend.title = element_blank(), #play around with turning these elements on and off one at a time
plot.background = element_rect(),
panel.background = element_rect(fill = 'white'), #color background of plot
panel.border = element_rect(fill = NA), #border of plot
panel.grid = element_blank(),
legend.key = element_blank(),
legend.position = 'top') +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors) ggplot(data, aes(yday, temp)) +
geom_point(aes(color = factor(mo))) +
xlab('Year Day') + ylab('Temperature') +
theme(legend.title = element_blank(), #play around with turning these elements on and off one at a time
plot.background = element_rect(),
panel.background = element_rect(fill = 'white'), #color background of plot
panel.border = element_rect(fill = NA), #border of plot
panel.grid = element_blank(),
legend.key = element_blank(),
legend.position = 'top',
axis.text = element_text(size = 10)) +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors)ggplot(data, aes(factor(mo), temp)) +
geom_boxplot(aes(color = factor(mo))) + #try geom_violin()!
xlab('Month') + ylab('Temperature') +
my_theme(legend.position = 'top') +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors)ggplot(data, aes(factor(mo), temp)) +
geom_boxplot(aes(color = factor(mo))) +
xlab('Month') + ylab('Temperature') +
my_theme(legend.position = 'top',
strip.background = element_blank()) +
facet_wrap(~year) +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors)ggplot(data_means %>% filter(site == 'Cliffside'),
aes(factor(year), temp, col = factor(mo), group = factor(mo))) +
geom_point() +
geom_line() +
xlab('') + ylab('Temperature') +
my_theme(legend.position = 'top',
strip.background = element_blank()) +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors)ggplot(data_means %>% filter(site == 'Cliffside'), #look at different sites, or facet by site below
aes(factor(year), temp, col = factor(mo), group = factor(mo))) +
geom_point(size = 0.8) +
geom_line(linetype = 'dashed') + #se = F, method = 'lm'
geom_smooth(aes(fill = factor(mo)), alpha = 0.2, show.legend = F) +
#or, instead, use geom_ribbon() if have own 95% CI
#geom_ribbon(aes(mean = mean, ymin = lower, ymax = upper)) +
xlab('') + ylab('Temperature') +
# facet_wrap(~site) +
my_theme(legend.position = 'top') +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors) +
scale_fill_manual(values = mycolors)ggplot(data_means,
aes(tide, count, group = year)) +
geom_point(size = 0.8) +
# geom_line(linetype = 'dashed') + #se = F, method = 'lm'
geom_smooth(method = 'lm') +
xlab('Tide Height') + ylab('Seabird count') +
facet_wrap(~year, scales = 'free_y')+
my_theme(legend.position = 'top',
strip.background = element_blank()) +
scale_fill_manual(values = mycolors)ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
facet_wrap(~continent) +
my_theme() +
# gganimate
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
ease_aes('linear')pnw_state_outline <- map_data("state", region = c("oregon", "washington"))
ggplot(pnw_state_outline) +
geom_polygon(aes(x = long, y = lat, group = group), fill = "grey93", color = "grey50", size = 0.2) +
coord_fixed(1, ylim = c(47, 49.15), xlim = c(-125, -121)) + #closer in
# coord_fixed(1, ylim = c(45, 49.15), xlim = c(-125, -116)) + #adjust these to zoom in/out; whole state
geom_point(data = data, aes(long, lat, color = temp)) +
xlab('') + ylab('') +
theme_classic() +
theme(
axis.line = element_blank(),
# axis.title = element_blank(), #put these two lines in if don't want axis labels/ticks
# axis.ticks = element_blank()
strip.text = element_text(size = 8),
strip.background = element_blank(),
legend.title = element_blank(),
legend.text = element_text(size = 16),
panel.border = element_rect(colour = "black", fill = NA, size = 1))us_pop <- map_data('state') %>%
merge(uspop_data, by = 'region')
ggplot(us_pop, aes(long, lat, group = group)) +
geom_polygon(aes(fill = population)) +
coord_fixed(1.3) + scale_fill_gradient(trans = 'log10') cowplota <- ggplot(us_pop, aes(long, lat, group = group)) +
geom_polygon(aes(fill = population)) +
coord_fixed(1.3) + scale_fill_gradient(trans = 'log10')
b <- ggplot(data_means %>% filter(site == 'Cliffside'),
aes(factor(year), temp, col = factor(mo), group = factor(mo))) +
geom_point() +
geom_line() +
xlab('') + ylab('Temperature') +
my_theme(legend.position = 'top',
strip.background = element_blank()) +
scale_color_manual(labels = c('May', 'June', 'July', 'August'), values = mycolors)
plot_grid(a, b, ncol = 2)Examples of manipulating everything
A master list of (sometimes ugly, but mind-opening) ggplots
Helpful outline of functionality per [specific geoms] (https://ggplot2.tidyverse.org/reference/)
Examples from R for Data Science